Overview

Dataset statistics

Number of variables8
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.2 KiB
Average record size in memory72.0 B

Variable types

Numeric7
Categorical1

Alerts

Process Out Temp has constant value ""Constant
HT1 Heat is highly overall correlated with Liquid Mass Flow and 2 other fieldsHigh correlation
Liquid Mass Flow is highly overall correlated with HT1 Heat and 2 other fieldsHigh correlation
Out Vapor Mass Flow is highly overall correlated with HT1 Heat and 2 other fieldsHigh correlation
Vapor Mass Flow is highly overall correlated with HT1 Heat and 2 other fieldsHigh correlation
Experiment is uniformly distributedUniform
Experiment has unique valuesUnique
HT1 Heat has unique valuesUnique
HT2 Heat has unique valuesUnique
T Inlet has unique valuesUnique
Out Vapor Mass Flow has unique valuesUnique
Vapor Mass Flow has 101 (20.2%) zerosZeros

Reproduction

Analysis started2024-03-11 17:55:33.714739
Analysis finished2024-03-11 17:55:41.492017
Duration7.78 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

Experiment
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-11T14:55:41.590759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.48183
Coefficient of variation (CV)0.57677378
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotonicityStrictly increasing
2024-03-11T14:55:41.769279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
330 1
 
0.2%
343 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
339 1
 
0.2%
338 1
 
0.2%
337 1
 
0.2%
336 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
500 1
0.2%
499 1
0.2%
498 1
0.2%
497 1
0.2%
496 1
0.2%
495 1
0.2%
494 1
0.2%
493 1
0.2%
492 1
0.2%
491 1
0.2%

HT1 Heat
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean655.91018
Minimum300
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-11T14:55:41.932811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile472.91626
Q1580.32668
median655.83466
Q3731.56644
95-th percentile838.47914
Maximum1000
Range700
Interquartile range (IQR)151.23976

Descriptive statistics

Standard deviation112.16761
Coefficient of variation (CV)0.17101062
Kurtosis-0.022397236
Mean655.91018
Median Absolute Deviation (MAD)75.853372
Skewness-0.0066597732
Sum327955.09
Variance12581.572
MonotonicityNot monotonic
2024-03-11T14:55:42.112365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
680.8733946 1
 
0.2%
624.7579958 1
 
0.2%
637.6046681 1
 
0.2%
562.8871122 1
 
0.2%
715.8798101 1
 
0.2%
677.1040037 1
 
0.2%
781.2016587 1
 
0.2%
568.5716742 1
 
0.2%
769.6379509 1
 
0.2%
645.3727389 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
300 1
0.2%
350.2456559 1
0.2%
359.7114512 1
0.2%
382.7979791 1
0.2%
393.7099131 1
0.2%
396.6592277 1
0.2%
405.9081463 1
0.2%
410.4775868 1
0.2%
418.0304675 1
0.2%
423.4023468 1
0.2%
ValueCountFrequency (%)
1000 1
0.2%
966.5808769 1
0.2%
942.7648254 1
0.2%
930.1310627 1
0.2%
919.9058581 1
0.2%
913.0318159 1
0.2%
905.0645046 1
0.2%
901.0583152 1
0.2%
893.088191 1
0.2%
888.8517177 1
0.2%

HT2 Heat
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean795.63419
Minimum604.0036
Maximum995.9964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-11T14:55:42.292940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum604.0036
5-th percentile687.64167
Q1751.21013
median795.78338
Q3840.2558
95-th percentile903.53698
Maximum995.9964
Range391.9928
Interquartile range (IQR)89.04567

Descriptive statistics

Standard deviation66.128592
Coefficient of variation (CV)0.083114317
Kurtosis-0.06299294
Mean795.63419
Median Absolute Deviation (MAD)44.50573
Skewness0.0010565702
Sum397817.09
Variance4372.9907
MonotonicityNot monotonic
2024-03-11T14:55:42.473427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
735.4370143 1
 
0.2%
813.2230525 1
 
0.2%
776.0614084 1
 
0.2%
877.0018288 1
 
0.2%
769.3431258 1
 
0.2%
788.0477583 1
 
0.2%
772.8498372 1
 
0.2%
995.9963985 1
 
0.2%
826.2155075 1
 
0.2%
759.0270431 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
604.0036015 1
0.2%
608.1057926 1
0.2%
628.2302404 1
0.2%
629.4536627 1
0.2%
637.3833838 1
0.2%
643.0700145 1
0.2%
647.3900354 1
0.2%
651.2082323 1
0.2%
656.1393089 1
0.2%
658.5131914 1
0.2%
ValueCountFrequency (%)
995.9963985 1
0.2%
980.1629501 1
0.2%
963.8246905 1
0.2%
958.0114431 1
0.2%
951.7292516 1
0.2%
945.1298445 1
0.2%
944.3429831 1
0.2%
940.2732114 1
0.2%
935.4910706 1
0.2%
933.724222 1
0.2%

T Inlet
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean341.50762
Minimum323
Maximum363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-11T14:55:42.652979image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum323
5-th percentile331.28002
Q1337.29951
median341.50646
Q3345.69102
95-th percentile351.73605
Maximum363
Range40
Interquartile range (IQR)8.391509

Descriptive statistics

Standard deviation6.2315235
Coefficient of variation (CV)0.0182471
Kurtosis0.026368354
Mean341.50762
Median Absolute Deviation (MAD)4.2021443
Skewness0.02409794
Sum170753.81
Variance38.831885
MonotonicityNot monotonic
2024-03-11T14:55:42.834462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
343.0231938 1
 
0.2%
348.7188461 1
 
0.2%
345.0676514 1
 
0.2%
332.9293265 1
 
0.2%
335.7763861 1
 
0.2%
342.9112209 1
 
0.2%
341.3266388 1
 
0.2%
339.6663612 1
 
0.2%
343.6625643 1
 
0.2%
336.4949415 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
323 1
0.2%
323.8897729 1
0.2%
325.5691041 1
0.2%
326.5045482 1
0.2%
326.6228483 1
0.2%
327.2695293 1
0.2%
327.699153 1
0.2%
327.9626832 1
0.2%
328.2280062 1
0.2%
328.4749312 1
0.2%
ValueCountFrequency (%)
363 1
0.2%
358.759362 1
0.2%
357.1794994 1
0.2%
356.6218433 1
0.2%
356.3161117 1
0.2%
355.6718444 1
0.2%
355.2094998 1
0.2%
355.0976843 1
0.2%
354.736963 1
0.2%
354.5092274 1
0.2%

Vapor Mass Flow
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct400
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.99327
Minimum0
Maximum697.73642
Zeros101
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-11T14:55:43.006037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q129.711137
median149.90281
Q3270.45073
95-th percentile440.63163
Maximum697.73642
Range697.73642
Interquartile range (IQR)240.73959

Descriptive statistics

Standard deviation148.59796
Coefficient of variation (CV)0.87414024
Kurtosis-0.1460561
Mean169.99327
Median Absolute Deviation (MAD)120.74146
Skewness0.69495142
Sum84996.634
Variance22081.353
MonotonicityNot monotonic
2024-03-11T14:55:43.189517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 101
 
20.2%
189.7588331 1
 
0.2%
419.3021909 1
 
0.2%
284.4112294 1
 
0.2%
120.8847782 1
 
0.2%
1.951278905 1
 
0.2%
245.4811522 1
 
0.2%
183.7588129 1
 
0.2%
349.4588044 1
 
0.2%
10.99981969 1
 
0.2%
Other values (390) 390
78.0%
ValueCountFrequency (%)
0 101
20.2%
1.951278905 1
 
0.2%
3.057933777 1
 
0.2%
4.003532814 1
 
0.2%
4.945031499 1
 
0.2%
7.015431834 1
 
0.2%
7.582701837 1
 
0.2%
9.49170821 1
 
0.2%
10.99981969 1
 
0.2%
11.79451443 1
 
0.2%
ValueCountFrequency (%)
697.7364221 1
0.2%
644.5407145 1
0.2%
606.6309313 1
0.2%
586.5208301 1
0.2%
570.2446103 1
0.2%
559.3026854 1
0.2%
546.6205224 1
0.2%
540.2435725 1
0.2%
527.5569321 1
0.2%
520.813422 1
0.2%

Liquid Mass Flow
Real number (ℝ)

HIGH CORRELATION 

Distinct400
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7030.0067
Minimum6502.2636
Maximum7200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-11T14:55:43.363085image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum6502.2636
5-th percentile6759.3684
Q16929.5493
median7050.0972
Q37170.2889
95-th percentile7200
Maximum7200
Range697.73642
Interquartile range (IQR)240.73959

Descriptive statistics

Standard deviation148.59796
Coefficient of variation (CV)0.021137669
Kurtosis-0.1460561
Mean7030.0067
Median Absolute Deviation (MAD)120.74146
Skewness-0.69495142
Sum3515003.4
Variance22081.353
MonotonicityNot monotonic
2024-03-11T14:55:43.533599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 101
 
20.2%
7010.241167 1
 
0.2%
6780.697809 1
 
0.2%
6915.588771 1
 
0.2%
7079.115222 1
 
0.2%
7198.048721 1
 
0.2%
6954.518848 1
 
0.2%
7016.241187 1
 
0.2%
6850.541196 1
 
0.2%
7189.00018 1
 
0.2%
Other values (390) 390
78.0%
ValueCountFrequency (%)
6502.263578 1
0.2%
6555.459286 1
0.2%
6593.369069 1
0.2%
6613.47917 1
0.2%
6629.75539 1
0.2%
6640.697315 1
0.2%
6653.379478 1
0.2%
6659.756428 1
0.2%
6672.443068 1
0.2%
6679.186578 1
0.2%
ValueCountFrequency (%)
7200 101
20.2%
7198.048721 1
 
0.2%
7196.942066 1
 
0.2%
7195.996467 1
 
0.2%
7195.054969 1
 
0.2%
7192.984568 1
 
0.2%
7192.417298 1
 
0.2%
7190.508292 1
 
0.2%
7189.00018 1
 
0.2%
7188.205486 1
 
0.2%

Process Out Temp
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size38.1 KiB
99.9823457963
500 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters6500
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99.9823457963
2nd row99.9823457963
3rd row99.9823457963
4th row99.9823457963
5th row99.9823457963

Common Values

ValueCountFrequency (%)
99.9823457963 500
100.0%

Length

2024-03-11T14:55:43.695202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-11T14:55:43.801915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
99.9823457963 500
100.0%

Most occurring characters

ValueCountFrequency (%)
9 2000
30.8%
3 1000
15.4%
. 500
 
7.7%
8 500
 
7.7%
2 500
 
7.7%
4 500
 
7.7%
5 500
 
7.7%
7 500
 
7.7%
6 500
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6000
92.3%
Other Punctuation 500
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 2000
33.3%
3 1000
16.7%
8 500
 
8.3%
2 500
 
8.3%
4 500
 
8.3%
5 500
 
8.3%
7 500
 
8.3%
6 500
 
8.3%
Other Punctuation
ValueCountFrequency (%)
. 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 2000
30.8%
3 1000
15.4%
. 500
 
7.7%
8 500
 
7.7%
2 500
 
7.7%
4 500
 
7.7%
5 500
 
7.7%
7 500
 
7.7%
6 500
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 2000
30.8%
3 1000
15.4%
. 500
 
7.7%
8 500
 
7.7%
2 500
 
7.7%
4 500
 
7.7%
5 500
 
7.7%
7 500
 
7.7%
6 500
 
7.7%

Out Vapor Mass Flow
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5765.4984
Minimum5185.2518
Maximum6439.8292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2024-03-11T14:55:43.923560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5185.2518
5-th percentile5416.52
Q15637.8416
median5763.7634
Q35907.7052
95-th percentile6087.8577
Maximum6439.8292
Range1254.5774
Interquartile range (IQR)269.86354

Descriptive statistics

Standard deviation207.19274
Coefficient of variation (CV)0.035936658
Kurtosis0.043986049
Mean5765.4984
Median Absolute Deviation (MAD)138.21061
Skewness-0.046371078
Sum2882749.2
Variance42928.833
MonotonicityNot monotonic
2024-03-11T14:55:44.099092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5714.256823 1
 
0.2%
5767.04372 1
 
0.2%
5716.609907 1
 
0.2%
5719.449703 1
 
0.2%
5800.722453 1
 
0.2%
5791.641954 1
 
0.2%
5928.067139 1
 
0.2%
5939.483707 1
 
0.2%
6002.100664 1
 
0.2%
5674.371019 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
5185.25182 1
0.2%
5187.50933 1
0.2%
5210.905872 1
0.2%
5227.537527 1
0.2%
5231.478833 1
0.2%
5237.659494 1
0.2%
5261.426963 1
0.2%
5268.181791 1
0.2%
5274.870064 1
0.2%
5312.482594 1
0.2%
ValueCountFrequency (%)
6439.829222 1
0.2%
6305.854277 1
0.2%
6297.429857 1
0.2%
6260.122994 1
0.2%
6232.179467 1
0.2%
6229.538424 1
0.2%
6219.300665 1
0.2%
6216.325487 1
0.2%
6215.775097 1
0.2%
6200.923939 1
0.2%

Interactions

2024-03-11T14:55:40.339046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:34.008926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.004272image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.908860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:37.426812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.683461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.522223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:40.438779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:34.115642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.147889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:36.050482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:37.714046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.794165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.630970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:40.549484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:34.226347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.276547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:36.191107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.023221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.914843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.750616image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:40.771890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:34.345099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.423164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:36.303807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.175814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.049516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.874317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:40.882627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:34.469699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.553807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:36.422522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.298487image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.173154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.998952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:40.989327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:34.698089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.667503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:36.542172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.419165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.287850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:40.118634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:41.113030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:34.859658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:35.789212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:36.761589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:38.560790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:39.404570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-11T14:55:40.232331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-11T14:55:44.306569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ExperimentHT1 HeatHT2 HeatLiquid Mass FlowOut Vapor Mass FlowT InletVapor Mass Flow
Experiment1.0000.054-0.071-0.0550.0100.0110.055
HT1 Heat0.0541.000-0.024-0.9960.8340.0200.996
HT2 Heat-0.071-0.0241.0000.0320.4850.032-0.032
Liquid Mass Flow-0.055-0.9960.0321.000-0.825-0.011-1.000
Out Vapor Mass Flow0.0100.8340.485-0.8251.0000.1270.825
T Inlet0.0110.0200.032-0.0110.1271.0000.011
Vapor Mass Flow0.0550.996-0.032-1.0000.8250.0111.000

Missing values

2024-03-11T14:55:41.261633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-11T14:55:41.421208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ExperimentHT1 HeatHT2 HeatT InletVapor Mass FlowLiquid Mass FlowProcess Out TempOut Vapor Mass Flow
01680.873395735.437014343.023194189.7588337010.24116799.9823465714.256823
12772.217853817.557849339.841386335.1586116864.84138999.9823465980.169619
23694.717981706.317650351.379087211.7962936988.20370799.9823465716.789765
34753.655326860.644904353.026370305.6112546894.38874699.9823466061.559708
45589.678615835.093289341.30966344.5973097155.40269199.9823465722.228740
56717.814191712.824553329.518728248.5602506951.43975099.9823465693.821052
67396.659228860.986463332.7257980.0000007200.00000099.9823465428.708509
78465.016790883.545814340.8331730.0000007200.00000099.9823465599.392694
89556.516807754.028910341.0597570.0000007200.00000099.9823465539.604974
910711.959098796.483956331.237802239.2402626960.75973899.9823465823.162874
ExperimentHT1 HeatHT2 HeatT InletVapor Mass FlowLiquid Mass FlowProcess Out TempOut Vapor Mass Flow
490491585.774154801.485236344.18833338.3822887161.61771299.9823465671.753489
491492434.058723701.585672331.7780300.0000007200.00000099.9823465231.478833
492493598.436534748.292841338.10043058.5379427141.46205899.9823465587.715314
493494599.145263786.015826332.31613659.6660797140.33392199.9823465630.373991
494495639.208425694.613960339.581475123.4375987076.56240299.9823465571.916513
495496574.115054749.696469343.96865019.8236317180.17636999.9823465570.053650
496497759.429074916.208107336.227813314.8017606885.19824099.9823466105.264392
497498804.310814853.675340337.934992386.2433686813.75663299.9823466082.635880
498499798.795422840.342505332.577192377.4641086822.53589299.9823466035.482795
499500686.296716745.710744333.218022198.3915387001.60846299.9823465707.827695